Image Inference with Runware API on New Scheduled Tasks from Pipedream API

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Image Inference with the Runware API

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Getting Started#

This integration creates a workflow with a Pipedream trigger and Runware action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Scheduled Tasks trigger
1. Connect your Pipedream account
2. Optional- Configure Secret
Configure the Image Inference action
1. Connect your Runware account
2. Select a Structure
3. Configure Model
4. Configure Positive Prompt
5. Configure Height
6. Configure Width
7. Optional- Configure Upload Endpoint
8. Optional- Configure Check NSFW
9. Optional- Configure Include Cost
10. Optional- Configure Scheduler
11. Optional- Configure Seed
12. Optional- Configure Number Of Results
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Details#

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger#

New Scheduled Tasks on Pipedream

Description:Exposes an HTTP API for scheduling messages to be emitted at a future time

Version:0.3.1

Key:pipedream-new-scheduled-tasks

View on GitHub

Pipedream Overview#

Pipedream is an API that allows you to build applications that can connect to
various data sources and processes them in real-time. You can use Pipedream to
create applications that can perform ETL (Extract, Transform, and Load) tasks,
as well as to create data-driven workflows.

Some examples of applications you can build using the Pipedream API include:

An application that can extract data from a database, transform it, and then
load it into another database.
An application that can monitor a data source for changes, and then trigger a
workflow in response to those changes.
An application that can poll an API for new data, and then process that data
in real-time.

Trigger Code#

import pipedream from "../../pipedream.app.mjs";
import sampleEmit from "./test-event.mjs";
import { uuid } from "uuidv4";

export default {
  key: "pipedream-new-scheduled-tasks",
  name: "New Scheduled Tasks",
  type: "source",
  description:
    "Exposes an HTTP API for scheduling messages to be emitted at a future time",
  version: "0.3.1",
  dedupe: "unique", // Dedupe on a UUID generated for every scheduled task
  props: {
    pipedream,
    secret: {
      type: "string",
      secret: true,
      label: "Secret",
      optional: true,
      description:
        "**Optional but recommended**: if you enter a secret here, you must pass this value in the `x-pd-secret` HTTP header when making requests",
    },
    http: {
      label: "Endpoint",
      description: "The endpoint where you'll send task scheduler requests",
      type: "$.interface.http",
      customResponse: true,
    },
    db: "$.service.db",
  },
  methods: {
    // To schedule future emits, we emit to the selfChannel of the component
    selfChannel() {
      return "self";
    },
    // Queue for future emits that haven't yet been delivered
    queuedEventsChannel() {
      return "$in";
    },
    httpRespond({
      status, body,
    }) {
      this.http.respond({
        headers: {
          "content-type": "application/json",
        },
        status,
        body,
      });
    },
    async selfSubscribe() {
      // Subscribe the component to itself. We do this here because even in
      // the activate hook, the component isn't available to take subscriptions.
      // Scheduled tasks are sent to the self channel, which emits the message at
      // the specified delivery_ts to this component.
      const isSubscribedToSelf = this.db.get("isSubscribedToSelf");
      if (!isSubscribedToSelf) {
        const componentId = process.env.PD_COMPONENT;
        const selfChannel = this.selfChannel();
        console.log(`Subscribing to ${selfChannel} channel for event source`);
        console.log(
          await this.pipedream.subscribe(componentId, componentId, selfChannel),
        );
        this.db.set("isSubscribedToSelf", true);
      }
    },
    validateEventBody(event, operation) {
      const errors = [];

      // Secrets are optional, so we first check if the user configured
      // a secret, then check its value against the prop (validation below)
      if (this.secret && event.headers["x-pd-secret"] !== this.secret) {
        errors.push(
          "Secret on incoming request doesn't match the configured secret",
        );
      }

      if (operation === "schedule") {
        const {
          timestamp,
          message,
        } = event.body;
        // timestamp should be an ISO 8601 string. Parse and check for validity below.
        const epoch = Date.parse(timestamp);

        if (!timestamp) {
          errors.push(
            "No timestamp included in payload. Please provide an ISO8601 timestamp in the 'timestamp' field",
          );
        }
        if (timestamp && !epoch) {
          errors.push("Timestamp isn't a valid ISO 8601 string");
        }
        if (!message) {
          errors.push("No message passed in payload");
        }
      }

      return errors;
    },
    scheduleTask(event) {
      const errors = this.validateEventBody(event, "schedule");
      let status, body;

      if (errors.length) {
        console.log(errors);
        status = 400;
        body = {
          errors,
        };
      } else {
        const id = this.emitScheduleEvent(event.body, event.body.timestamp);
        status = 200;
        body = {
          msg: "Successfully scheduled task",
          id,
        };
      }

      this.httpRespond({
        status,
        body,
      });
    },
    emitScheduleEvent(event, timestamp) {
      const selfChannel = this.selfChannel();
      const epoch = Date.parse(timestamp);
      const $id = uuid();

      console.log(`Scheduled event to emit on: ${new Date(epoch)}`);

      this.$emit(
        {
          ...event,
          $channel: selfChannel,
          $id,
        },
        {
          name: selfChannel,
          id: $id,
          delivery_ts: epoch,
        },
      );

      return $id;
    },
    async cancelTask(event) {
      const errors = this.validateEventBody(event, "cancel");
      let status, msg;

      if (errors.length) {
        console.log(errors);
        status = 400;
        msg = "Secret on incoming request doesn't match the configured secret";
      } else {
        try {
          const id = event.body.id;
          const isCanceled = await this.deleteEvent(event);
          if (isCanceled) {
            status = 200;
            msg = `Cancelled scheduled task for event ${id}`;
          } else {
            status = 404;
            msg = `No event with ${id} found`;
          }
        } catch (error) {
          console.log(error);
          status = 500;
          msg = "Failed to schedule task. Please see the logs";
        }
      }

      this.httpRespond({
        status,
        body: {
          msg,
        },
      });
    },
    async deleteEvent(event) {
      const componentId = process.env.PD_COMPONENT;
      const inChannel = this.queuedEventsChannel();

      // The user must pass a scheduled event UUID they'd like to cancel
      // We lookup the event by ID and delete it
      const { id } = event.body;

      // List events in the $in channel - the queue of scheduled events, to be emitted in the future
      const events = await this.pipedream.listEvents(
        componentId,
        inChannel,
      );
      console.log("Events: ", events);

      // Find the event in the list by id
      const eventToCancel = events.data.find((e) => {
        const { metadata } = e;
        return metadata.id === id;
      });

      console.log("Event to cancel: ", eventToCancel);

      if (!eventToCancel) {
        console.log(`No event with ${id} found`);
        return false;
      }

      // Delete the event
      await this.pipedream.deleteEvent(
        componentId,
        eventToCancel.id,
        inChannel,
      );
      return true;
    },
    emitEvent(event, summary) {
      // Delete the channel name and id from the incoming event, which were used only as metadata
      const id = event.$id;
      delete event.$channel;
      delete event.$id;

      this.$emit(event, {
        summary: summary ?? JSON.stringify(event),
        id,
        ts: +new Date(),
      });
    },
  },
  async run(event) {
    await this.selfSubscribe();

    const { path } = event;
    if (path === "/schedule") {
      this.scheduleTask(event);
    } else if (path === "/cancel") {
      await this.cancelTask(event);
    } else if (event.$channel === this.selfChannel()) {
      this.emitEvent(event);
    }
  },
  sampleEmit,
};

Trigger Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
Pipedream	`pipedream`	`app`	This component uses the Pipedream app.
Secret	`secret`	`string`	Optional but recommended: if you enter a secret here, you must pass this value in the `x-pd-secret` HTTP header when making requests
N/A	`http`	`$.interface.http`	This component uses `$.interface.http` to generate a unique URL when the component is first instantiated. Each request to the URL will trigger the `run()` method of the component.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.

Trigger Authentication#

Pipedream uses API keys for authentication. When you connect your Pipedream account, Pipedream securely stores the keys so you can easily authenticate to Pipedream APIs in both code and no-code steps.

About Pipedream#

Integration platform for developers

Action#

Image Inference on Runware

Description:Request an image inference task to be processed by the Runware API. [See the documentation](https://docs.runware.ai/en/image-inference/api-reference).

Version:0.0.1

Key:runware-image-inference

View on GitHub

Action Code#

import { v4 as uuid } from "uuid";
import app from "../../runware.app.mjs";
import constants from "../../common/constants.mjs";

export default {
  key: "runware-image-inference",
  name: "Image Inference",
  description: "Request an image inference task to be processed by the Runware API. [See the documentation](https://docs.runware.ai/en/image-inference/api-reference).",
  version: "0.0.1",
  type: "action",
  props: {
    app,
    structure: {
      type: "string",
      label: "Structure",
      description: "The structure of the task to be processed.",
      options: Object.values(constants.IMAGE_INFERENCE_STRUCTURE),
      reloadProps: true,
    },
    model: {
      type: "string",
      label: "Model",
      description: "This identifier is a unique string that represents a specific model. You can find the AIR identifier of the model you want to use in our [Model Explorer](https://docs.runware.ai/en/image-inference/models#model-explorer), which is a tool that allows you to search for models based on their characteristics. More information about the AIR system can be found in the [Models page](https://docs.runware.ai/en/image-inference/models). Eg. `civitai:78605@83390`.",
    },
    positivePrompt: {
      type: "string",
      label: "Positive Prompt",
      description: "A positive prompt is a text instruction to guide the model on generating the image. It is usually a sentence or a paragraph that provides positive guidance for the task. This parameter is essential to shape the desired results. For example, if the positive prompt is `dragon drinking coffee`, the model will generate an image of a dragon drinking coffee. The more detailed the prompt, the more accurate the results. The length of the prompt must be between 4 and 2000 characters.",
    },
    height: {
      propDefinition: [
        app,
        "height",
      ],
    },
    width: {
      propDefinition: [
        app,
        "width",
      ],
    },
    uploadEndpoint: {
      type: "string",
      label: "Upload Endpoint",
      description: "This parameter allows you to specify a URL to which the generated image will be uploaded as binary image data using the HTTP PUT method. For example, an S3 bucket URL can be used as the upload endpoint. When the image is ready, it will be uploaded to the specified URL.",
      optional: true,
    },
    checkNSFW: {
      type: "boolean",
      label: "Check NSFW",
      description: "This parameter is used to enable or disable the NSFW check. When enabled, the API will check if the image contains NSFW (not safe for work) content. This check is done using a pre-trained model that detects adult content in images. When the check is enabled, the API will return `NSFWContent: true` in the response object if the image is flagged as potentially sensitive content. If the image is not flagged, the API will return `NSFWContent: false`. If this parameter is not used, the parameter `NSFWContent` will not be included in the response object. Adds `0.1` seconds to image inference time and incurs additional costs. The NSFW filter occasionally returns false positives and very rarely false negatives.",
      optional: true,
    },
    includeCost: {
      propDefinition: [
        app,
        "includeCost",
      ],
    },
    scheduler: {
      type: "string",
      label: "Scheduler",
      description: "An scheduler is a component that manages the inference process. Different schedulers can be used to achieve different results like more detailed images, faster inference, or more accurate results. The default scheduler is the one that the model was trained with, but you can choose a different one to get different results. Schedulers are explained in more detail in the [Schedulers page](https://docs.runware.ai/en/image-inference/schedulers).",
      optional: true,
    },
    seed: {
      type: "string",
      label: "Seed",
      description: "A seed is a value used to randomize the image generation. If you want to make images reproducible (generate the same image multiple times), you can use the same seed value. When requesting multiple images with the same seed, the seed will be incremented by 1 (+1) for each image generated. Min: `0` Max: `9223372036854776000`. Defaults to `Random`.",
      optional: true,
    },
    numberResults: {
      type: "integer",
      label: "Number Of Results",
      description: "The number of images to generate from the specified prompt. If **Seed** is set, it will be incremented by 1 (+1) for each image generated.",
      optional: true,
    },
  },
  additionalProps() {
    const { structure } = this;

    const seedImage = {
      type: "string",
      label: "Seed Image",
      description: "When doing Image-to-Image, Inpainting or Outpainting, this parameter is **required**. Specifies the seed image to be used for the diffusion process. The image can be specified in one of the following formats:\n - An UUID v4 string of a [previously uploaded image](https://docs.runware.ai/en/getting-started/image-upload) or a [generated image](https://docs.runware.ai/en/image-inference/api-reference).\n - A data URI string representing the image. The data URI must be in the format `data:<mediaType>;base64,` followed by the base64-encoded image. For example: `data:image/png;base64,iVBORw0KGgo...`.\n - A base64 encoded image without the data URI prefix. For example: `iVBORw0KGgo...`.\n - A URL pointing to the image. The image must be accessible publicly. Supported formats are: PNG, JPG and WEBP.",
    };

    const maskImage = {
      type: "string",
      label: "Mask Image",
      description: "When doing Inpainting or Outpainting, this parameter is **required**. Specifies the mask image to be used for the inpainting process. The image can be specified in one of the following formats:\n - An UUID v4 string of a [previously uploaded image](https://docs.runware.ai/en/getting-started/image-upload) or a [generated image](https://docs.runware.ai/en/image-inference/api-reference).\n - A data URI string representing the image. The data URI must be in the format `data:<mediaType>;base64,` followed by the base64-encoded image. For example: `data:image/png;base64,iVBORw0KGgo...`.\n - A base64 encoded image without the data URI prefix. For example: `iVBORw0KGgo...`.\n - A URL pointing to the image. The image must be accessible publicly. Supported formats are: PNG, JPG and WEBP.",
    };

    const strength = {
      type: "string",
      label: "Strength",
      description: "When doing Image-to-Image, Inpainting or Outpainting, this parameter is used to determine the influence of the **Seed Image** image in the generated output. A higher value results in more influence from the original image, while a lower value allows more creative deviation. Min: `0` Max: `1` and Default: `0.8`.",
      optional: true,
    };

    const controlNetModel = {
      type: "string",
      label: "ControlNet Model 0",
      description: "For basic/common ControlNet models, you can check the list of available models [here](https://docs.runware.ai/en/image-inference/models#basic-controlnet-models). For custom or specific ControlNet models, we make use of the [AIR system](https://github.com/civitai/civitai/wiki/AIR-%E2%80%90-Uniform-Resource-Names-for-AI) to identify ControlNet models. This identifier is a unique string that represents a specific model. You can find the AIR identifier of the ControlNet model you want to use in our [Model Explorer](https://docs.runware.ai/en/image-inference/models#model-explorer), which is a tool that allows you to search for models based on their characteristics. More information about the AIR system can be found in the [Models page](https://docs.runware.ai/en/image-inference/models).",
    };

    const controlNetGuideImage = {
      type: "string",
      label: "ControlNet Guide Image 0",
      description: "The guide image for ControlNet.",
    };

    const controlNetWeight = {
      type: "integer",
      label: "ControlNet Weight 0",
      description: "The weight for ControlNet.",
    };

    const controlNetStartStep = {
      type: "integer",
      label: "ControlNet Start Step 0",
      description: "The start step for ControlNet.",
    };

    const controlNetEndStep = {
      type: "integer",
      label: "ControlNet End Step 0",
      description: "The end step for ControlNet.",
    };

    const controlNetControlMode = {
      type: "string",
      label: "ControlNet Control Mode 0",
      description: "The control mode for ControlNet.",
    };

    const loraModel = {
      type: "string",
      label: "LoRA Model 0",
      description: "We make use of the [AIR system](https://github.com/civitai/civitai/wiki/AIR-%E2%80%90-Uniform-Resource-Names-for-AI) to identify LoRA models. This identifier is a unique string that represents a specific model. You can find the AIR identifier of the LoRA model you want to use in our [Model Explorer](https://docs.runware.ai/en/image-inference/models#model-explorer), which is a tool that allows you to search for models based on their characteristics. More information about the AIR system can be found in the [Models page](https://docs.runware.ai/en/image-inference/models).",
    };

    const loraWeight = {
      type: "integer",
      label: "LoRA Weight 0",
      description: "It is possible to use multiple LoRAs at the same time. With the `weight` parameter you can assign the importance of the LoRA with respect to the others. The sum of all `weight` parameters must always be `1`. If needed, we will increase the values proportionally to achieve it.",
      optional: true,
    };

    if (structure === constants.IMAGE_INFERENCE_STRUCTURE.TEXT_TO_IMAGE.value) {
      return {
        outputType: {
          type: "string",
          label: "Output Type",
          description: "Specifies the output type in which the image is returned.",
          optional: true,
          options: [
            "base64Data",
            "dataURI",
            "URL",
          ],
        },
        outputFormat: {
          type: "string",
          label: "Output Format",
          description: "Specifies the format of the output image.",
          optional: true,
          options: [
            "PNG",
            "JPG",
            "WEBP",
          ],
        },
        negativePrompt: {
          type: "string",
          label: "Negative Prompt",
          description: "A negative prompt is a text instruction to guide the model on generating the image. It is usually a sentence or a paragraph that provides negative guidance for the task. This parameter helps to avoid certain undesired results. For example, if the negative prompt is `red dragon, cup`, the model will follow the positive prompt but will avoid generating an image of a red dragon or including a cup. The more detailed the prompt, the more accurate the results. The length of the prompt must be between 4 and 2000 characters.",
          optional: true,
        },
        steps: {
          type: "integer",
          label: "Steps",
          description: "The number of steps is the number of iterations the model will perform to generate the image. The higher the number of steps, the more detailed the image will be. However, increasing the number of steps will also increase the time it takes to generate the image and may not always result in a better image (some [schedulers](https://docs.runware.ai/en/image-inference/api-reference#request-scheduler) work differently). When using your own models you can specify a new default value for the number of steps. Defaults to `20`.",
          min: 1,
          max: 100,
          optional: true,
        },
        CFGScale: {
          type: "string",
          label: "CFG Scale",
          description: "Guidance scale represents how closely the images will resemble the prompt or how much freedom the AI model has. Higher values are closer to the prompt. Low values may reduce the quality of the results. Min: `0`, Max: `30` Default: `7`.",
          optional: true,
        },
      };
    }

    if (structure === constants.IMAGE_INFERENCE_STRUCTURE.IMAGE_TO_IMAGE.value) {
      return {
        seedImage,
        strength,
      };
    }

    if (structure === constants.IMAGE_INFERENCE_STRUCTURE.IN_OUT_PAINTING.value) {
      return {
        seedImage,
        maskImage,
        strength,
      };
    }

    if (structure === constants.IMAGE_INFERENCE_STRUCTURE.REFINER.value) {
      return {
        refinerModel: {
          type: "string",
          label: "Refiner Model",
          description: "We make use of the [AIR system](https://github.com/civitai/civitai/wiki/AIR-%E2%80%90-Uniform-Resource-Names-for-AI) to identify refinement models. This identifier is a unique string that represents a specific model. Note that refiner models are only SDXL based. You can find the AIR identifier of the refinement model you want to use in our [Model Explorer](https://docs.runware.ai/en/image-inference/models#model-explorer), which is a tool that allows you to search for models based on their characteristics. More information about the AIR system can be found in the [Models page](https://docs.runware.ai/en/image-inference/models).",
        },
        refinerStartStep: {
          type: "integer",
          label: "Refiner Start Step",
          description: "Represents the step number at which the refinement process begins. The initial model will generate the image up to this step, after which the refiner model takes over to enhance the result. It can take values from `0` (first step) to the number of [steps](https://docs.runware.ai/en/image-inference/api-reference#request-steps) specified.",
          optional: true,
        },
      };
    }

    if (structure === constants.IMAGE_INFERENCE_STRUCTURE.CONTROL_NET.value) {
      return {
        controlNetModel1: {
          ...controlNetModel,
          label: "Control Net Model 1",
        },
        controlNetGuideImage1: {
          ...controlNetGuideImage,
          label: "Control Net Guide Image 1",
        },
        controlNetWeight1: {
          ...controlNetWeight,
          label: "Control Net Weight 1",
        },
        controlNetStartStep1: {
          ...controlNetStartStep,
          label: "Control Net Start Step 1",
        },
        controlNetEndStep1: {
          label: "Control Net End Step 1",
          ...controlNetEndStep,
        },
        controlNetControlMode1: {
          ...controlNetControlMode,
          label: "Control Net Control Mode 1",
        },
        controlNetModel2: {
          ...controlNetModel,
          label: "Control Net Model 2",
          optional: true,
        },
        controlNetGuideImage2: {
          ...controlNetGuideImage,
          label: "Control Net Guide Image 2",
          optional: true,
        },
        controlNetWeight2: {
          ...controlNetWeight,
          label: "Control Net Weight 2",
          optional: true,
        },
        controlNetStartStep2: {
          ...controlNetStartStep,
          label: "Control Net Start Step 2",
          optional: true,
        },
        controlNetEndStep2: {
          ...controlNetEndStep,
          label: "Control Net End Step 2",
          optional: true,
        },
        controlNetControlMode2: {
          ...controlNetControlMode,
          label: "Control Net Control Mode 2",
          optional: true,
        },
      };
    }

    if (structure === constants.IMAGE_INFERENCE_STRUCTURE.LORA.value) {
      return {
        loraModel1: {
          ...loraModel,
          label: "LoRA Model 1",
        },
        loraWeight1: {
          label: "LoRA Weight 1",
          ...loraWeight,
        },
        loraModel2: {
          label: "LoRA Model 2",
          ...loraModel,
          optional: true,
        },
        loraWeight2: {
          label: "LoRA Weight 2",
          ...loraWeight,
        },
      };
    }

    return {};
  },
  async run({ $ }) {
    const {
      app,
      outputType,
      outputFormat,
      uploadEndpoint,
      checkNSFW,
      includeCost,
      positivePrompt,
      negativePrompt,
      seedImage,
      maskImage,
      strength,
      height,
      width,
      model,
      steps,
      scheduler,
      seed,
      numberResults,
      CFGScale,
      refinerModel,
      refinerStartStep,
      controlNetModel1,
      controlNetGuideImage1,
      controlNetWeight1,
      controlNetStartStep1,
      controlNetEndStep1,
      controlNetControlMode1,
      controlNetModel2,
      controlNetGuideImage2,
      controlNetWeight2,
      controlNetStartStep2,
      controlNetEndStep2,
      controlNetControlMode2,
      loraModel1,
      loraWeight1,
      loraModel2,
      loraWeight2,
    } = this;

    const data = {
      taskType: constants.TASK_TYPE.IMAGE_INFERENCE.value,
      taskUUID: uuid(),
      positivePrompt,
      outputType,
      outputFormat,
      uploadEndpoint,
      checkNSFW,
      includeCost,
      negativePrompt,
      seedImage,
      maskImage,
      strength,
      height,
      width,
      model,
      steps,
      scheduler,
      seed: seed
        ? parseInt(seed)
        : undefined,
      numberResults,
      CFGScale,
      refiner: refinerModel
        ? {
          model: refinerModel,
          startStep: refinerStartStep,
        }
        : undefined,
      controlNet: controlNetModel1
        ? [
          {
            model: controlNetModel1,
            guideImage: controlNetGuideImage1,
            weight: controlNetWeight1,
            startStep: controlNetStartStep1,
            endStep: controlNetEndStep1,
            controlMode: controlNetControlMode1,
          },
          ...(controlNetModel2
            ? [
              {
                model: controlNetModel2,
                guideImage: controlNetGuideImage2,
                weight: controlNetWeight2,
                startStep: controlNetStartStep2,
                endStep: controlNetEndStep2,
                controlMode: controlNetControlMode2,
              },
            ]
            : []
          ),
        ]
        : undefined,
      lora: loraModel1
        ? [
          {
            model: loraModel1,
            weight: loraWeight1,
          },
          ...(loraModel2
            ? [
              {
                model: loraModel2,
                weight: loraWeight2,
              },
            ]
            : []
          ),
        ]
        : undefined,
    };

    const response = await app.post({
      $,
      data: [
        data,
      ],
    });

    $.export("$summary", `Successfully requested image inference task with UUID \`${response.data[0].taskUUID}\`.`);
    return response;
  },
};

Action Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

Label	Prop	Type	Description
Runware	`app`	`app`	This component uses the Runware app.
Structure	`structure`	`string`	Select a value from the drop down menu:`{ "value": "textToImage", "label": "Text to Image" }{ "value": "imageToImage", "label": "Image to Image" }{ "value": "inOutpainting", "label": "In/Outpainting" }{ "value": "refiner", "label": "Refiner" }{ "value": "controlNet", "label": "Control Net" }{ "value": "lora", "label": "LoRA" }`
Model	`model`	`string`	This identifier is a unique string that represents a specific model. You can find the AIR identifier of the model you want to use in our Model Explorer, which is a tool that allows you to search for models based on their characteristics. More information about the AIR system can be found in the Models page. Eg. `civitai:78605@83390`.
Positive Prompt	`positivePrompt`	`string`	A positive prompt is a text instruction to guide the model on generating the image. It is usually a sentence or a paragraph that provides positive guidance for the task. This parameter is essential to shape the desired results. For example, if the positive prompt is `dragon drinking coffee`, the model will generate an image of a dragon drinking coffee. The more detailed the prompt, the more accurate the results. The length of the prompt must be between 4 and 2000 characters.
Height	`height`	`integer`	Used to define the height dimension of the generated image. Certain models perform better with specific dimensions. The value must be divisible by 64, eg: `512`, `576`, `640` ... `2048`.
Width	`width`	`integer`	Used to define the width dimension of the generated image. Certain models perform better with specific dimensions. The value must be divisible by 64, eg: `512`, `576`, `640` ... `2048`.
Upload Endpoint	`uploadEndpoint`	`string`	This parameter allows you to specify a URL to which the generated image will be uploaded as binary image data using the HTTP PUT method. For example, an S3 bucket URL can be used as the upload endpoint. When the image is ready, it will be uploaded to the specified URL.
Check NSFW	`checkNSFW`	`boolean`	This parameter is used to enable or disable the NSFW check. When enabled, the API will check if the image contains NSFW (not safe for work) content. This check is done using a pre-trained model that detects adult content in images. When the check is enabled, the API will return `NSFWContent: true` in the response object if the image is flagged as potentially sensitive content. If the image is not flagged, the API will return `NSFWContent: false`. If this parameter is not used, the parameter `NSFWContent` will not be included in the response object. Adds `0.1` seconds to image inference time and incurs additional costs. The NSFW filter occasionally returns false positives and very rarely false negatives.
Include Cost	`includeCost`	`boolean`	If set to `true`, the cost to perform the task will be included in the response object. Defaults to `false`.
Scheduler	`scheduler`	`string`	An scheduler is a component that manages the inference process. Different schedulers can be used to achieve different results like more detailed images, faster inference, or more accurate results. The default scheduler is the one that the model was trained with, but you can choose a different one to get different results. Schedulers are explained in more detail in the Schedulers page.
Seed	`seed`	`string`	A seed is a value used to randomize the image generation. If you want to make images reproducible (generate the same image multiple times), you can use the same seed value. When requesting multiple images with the same seed, the seed will be incremented by 1 (+1) for each image generated. Min: `0` Max: `9223372036854776000`. Defaults to `Random`.
Number Of Results	`numberResults`	`integer`	The number of images to generate from the specified prompt. If Seed is set, it will be incremented by 1 (+1) for each image generated.

Action Authentication#

Runware uses API keys for authentication. When you connect your Runware account, Pipedream securely stores the keys so you can easily authenticate to Runware APIs in both code and no-code steps.

About Runware#

Low Cost, Ultra-Fast Stable Diffusion API

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